Confidence Level Of Identification Research Articles

Non-target screening and human risk assessment for adult and child populations of semi-volatile organic compounds in residential indoor dust in Spain

In this work, an analytical strategy based on non-target screening of semi-volatile organic compounds and subsequent risk assessment for adult and child populations has been conducted for the first time in household indoor dust samples in Spain. The methodology was based on a microwave-assisted extraction followed by gas chromatography coupled to high resolution mass spectrometry determination, using a hybrid quadrupole-orbitrap analyzer. The procedure was applied to 19 residential indoor dust samples, collected in different Spanish regions (namely Galicia, La Rioja, Catalunya, the Balearic Islands, and the Valencian Region). From the generated data, 4067 features were obtained, of which 474 compounds were tentatively identified with a high level of identification confidence (probable structure by library spectrum match or confirmed by reference standard), using a restrictive set of identification criteria. Most of the identified chemicals were natural products, metabolites, additives, and substances with industrial applications in the field of foods, cosmetics, pharmaceuticals, pesticides, and plastics. Finally, risk assessment was carried out by applying the threshold of toxicological concern approach, showing that risk to adult and child populations associated with the presence of the identified substances in the indoor dust was not expected, although the existence of indoor environments with conditions of potential risk cannot be discarded under a worst-case scenario approach.

Potential and limitation of retrospective HRMS based data analysis: “Have meat-producing animals been exposed to illegal growth promotors such as SARMs?”

Targeted strategies using gas or liquid chromatography coupled to triple quadrupole mass analyzers are mostly used in food safety control laboratories. However, when a new chemical hazard emerges, the target list needs to be updated and thus the should be reanalyzed. Under this scenario, the constant emergence of compounds that can illegally be used as growth promotors in cattle, such as selective androgen receptor modulators (SARM), and the phasing out of older SARMs, would require routine control laboratories (RCL) to constantly re-develop and re-validate their once developed targeted methods. Nevertheless, high-resolution mass spectrometry (HRMS) is not limited to analyzing samples using only a predefined targeted list and therefore the implementation of untargeted HRMS methods in RCLs to detect SARMs would enhance the laboratories throughput. In this study, two HRMS profile databases of cattle urine samples gathered over the past years and collected by two independent laboratories, were retrospectively analyzed to find out if a new SARM has been used. This study assesses different retrospective screening approaches based on the Schymanski identification confidence levels. A screening purely based on identification confidence levels 4 and 5 does not permit reliable detection of any exogenous trace compound that does not belong to a certain food or environmental matrix sample due to the high number of false detects. Therefore, it is shown that the availability of a physical reference substance to increase the identification confidence up to level 1 is essential to discriminate a suspected detection between a false positive detection or a confirmed finding.

Identifying antimicrobials and their metabolites in wastewater and surface water with effect-directed analysis

This study aimed to identify antimicrobial contaminants in the aquatic environment with effect-directed analysis. Wastewater influent, effluent, and surface water (up- and downstream of the discharge location) were sampled at two study sites. The samples were enriched, subjected to high-resolution fractionation, and the resulting 80 fractions were tested in an antibiotics bioassay. The resulting bioactive fractions guided the suspect and nontargeted identification strategy in the high-resolution mass spectrometry data that was recorded in parallel. Chemical features were annotated with reference databases, assessed on annotation quality, and assigned identification confidence levels. To identify antibiotic metabolites, Phase I metabolites were predicted in silico for over 500 antibiotics and included as a suspect list. Predicted retention times and fragmentation patterns reduced the number of annotations to consider for confirmation testing. Overall, the bioactivity of three fractions could be explained by the identified antibiotics (clarithromycin and azithromycin) and an antibiotic metabolite (14-OH(R) clarithromycin), explaining 78% of the bioactivity measured at one study site. The applied identification strategy successfully identified antibiotic metabolites in the aquatic environment, emphasizing the need to include the toxic effects of bioactive metabolites in environmental risk assessments.

HRMS screening of organophosphate flame retardants and poly-/perfluorinated substances in dust from cars and trucks: Occurrence and human exposure implications

Time spent within vehicles' cabin has been largely increased during the last years. As a result, the assessment of indoor dust quality is meaningful since dust can be a source of numerous emerging contaminants associated with adverse effects in human health. To this end, fourteen cars and ten trucks from the city of Thessaloniki, Northern Greece were selected to assess the quality of vehicles' microenvironments. An HRMS-based strategy was deployed for the target and non-target analysis of the collected samples. The target approach aimed at the accurate mass screening of nine organophosphate flame retardants (OPFRs) and nine per-/polyfluorinated compounds (PFAS), revealing mean concentrations for the OPFRs varied from <MQL–3409 ng/g for tris(1,3-dichloro-2-propyl) phosphate (TDCP), while the PFASs were either not detected (<MDL) or detected below the quantification limit (<MQL). To exploit the advanced technology of HRMS, a non-target analysis (NTA) workflow was also designed and employed, allowing the identification of 17 non-targets (plasticizers, PPCPs, pesticides and industrial chemicals) at identification confidence levels from 3 to 1. The statistical analysis between the positive findings and vehicles' conditions evidenced a possible of association just for individual cases. Lastly, a preliminary evaluation of human exposure to the target analytes was applied with the view to assess the potential harmful effects. All values were < 1 indicating no special effects because of exposure to this concentration level.

Simple and Reproducible Derivatization with Benzoyl Chloride: Improvement of Sensitivity for Multiple Lipid Classes in RP-UHPLC/MS.

The chemical derivatization of multiple lipid classes was developed using benzoyl chloride as a nonhazardous derivatization agent at ambient conditions. The derivatization procedure was optimized with standards for 4 nonpolar and 8 polar lipid classes and measured by reversed-phase ultrahigh-performance liquid chromatography-tandem mass spectrometry. The derivatization and nonderivatization approaches were compared on the basis of the calibration curves of 22 internal standards from 12 lipid classes. The new method decreased the limit of detection 9-fold for monoacylglycerols (0.9-1.0 nmol/mL), 6.5-fold for sphingoid base (0.2 nmol/mL), and 3-fold for diacylglycerols (0.9 nmol/mL). The sensitivity expressed by the ratio of calibration slopes was increased 2- to 10-fold for almost all investigated lipid classes and even more than 100-fold for monoacylglycerols. Moreover, the benzoylation reaction produces a more stable derivative of cholesterol in comparison to the easily in-source fragmented nonderivatized form and enabled the detection of fatty acids in a positive ion mode, which does not require polarity switching as for the nonderivatized form. The intralaboratory comparison with an additional operator without previous derivatization experiences shows the simplicity, robustness, and reproducibility. The stability of the derivatives was determined by periodical measurements during a one month period and five freeze/thaw cycles. The fully optimized derivatization method was applied to human plasma, which allows the detection of 169 lipid species from 11 lipid classes using the high confidence level of identification in reversed-phase (RP)-ultra high performance liquid chromatography (UHPLC)/mass spectrometry (MS). Generally, we detected more lipid species for monoacylglycerols, diacylglycerols, and sphingoid bases in comparison with previously reported papers without the derivatization.

An integrated approach to MS-based identification and risk assessment of pharmaceutical biotransformation in wastewater

The omnipresence of pharmaceuticals at relatively high concentrations (μg/L) in environmental compartments indicated their inadequate removal by wastewater treatment plants. As such, batch reactors seeded with activated sludge were set up to assess the biotransformation of metformin, ranitidine, lidocaine and atorvastatin. The main objective was to identify transformation products (TPs) through the establishment of an integrated workflow for suspect and non-target screening based on reversed phase liquid chromatography quadrupole-time-of-flight mass spectrometry. To support the identification, hydrophilic interaction liquid chromatography (HILIC) was used as a complementary tool, in order to enhance the completeness of the developed workflow by identifying the more polar TPs. The structure assignment/elucidation of the candidate TPs was mainly based on interpretation of MS/MS spectra. Twenty-two TPs were identified, with fourteen of them reaching high identification confidence levels (level 1: confirmed structure by reference standards and level 2: probable structure by library spectrum match and diagnostic evidence). Finally, retrospective analysis in influent and effluent wastewater was performed for the TPs for four consecutive years in wastewater sampled in Athens, Greece. The potential toxicological threat of the compounds to the aquatic environment was assessed and atorvastatin with two of its TPs showed a potential risk to the aquatic organisms.

Enhancing the identification accuracy of deep learning object detection using natural language processing

In recent years, object detection technology with artificial intelligence has been applied in many fields. This study uses a deep learning method to train an identification model to classify and browse pictures of the 600 different kinds of birds in Taiwan. To enhance the accuracy of identification and classification of these birds, we propose an automatic extraction system that can obtain training data by visiting public social media pages. We also develop mobile apps that allow users to take pictures of birds and upload them to an identification server to enable real-time identification and provide training data. These mobile apps are sent candidate bird pictures by the identification server to allow users to confirm and give feedback when the confidence level of identification is within a critical range. The bird pictures are then used as training data, and the identification model is periodically retrained to optimise the model. We also use natural language processing technology to enhance the level of confidence in image identification. The features of the birds’ appearance are described in words and candidate birds are obtained through image identification and used to readjust the adopted weight values. The proposed identification system gives a relatively high identification accuracy due to the use of deep learning object detection.

Liquid chromatography-tandem mass spectrometry analysis for identification and quantification of antimicrobial compounds in distillery wastewater

A high-resolution mass spectrometry (HR-MS) method was developed to analyze and identify small molecule compounds in distillery wastewater. According to identification confidence levels, 4 levels of compounds were identified. The five antimicrobial compounds (lactic acid, succinic acid, acetophenone, cinnamic acid, and phenyllactic acid), which shown in high concentrations, were at the highest level of confidence (level 1, confirmed structure). Thus, a rapid and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed to simultaneously quantify these antimicrobial compounds. The analysis was performed in the selective reaction monitoring (SRM) mode via the electrospray ionization (ESI) source operating in the negative ionization mode. Linear calibration curves were obtained over the concentration range of 50–1000 ng/mL for succinic acid, acetophenone, cinnamic acid, phenyllactic acid, and 375–7500 ng/mL for lactic acid. Precision and recovery of the analytes were all satisfactory (relative standard deviation < 10%). The validated method was successfully applied to quantitative analysis of the five antimicrobial compounds in distillery wastewater.•Analyze and identify 4 levels of small molecule compounds in distillery wastewater.•Simple method for quantification of five antimicrobial compounds.•Column temperature affected the lactic and succinic acid chromatographs significantly.

Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation.

Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS-HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS-HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed. Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS-HRMS and to demonstrate the additional value of CCS data in various screening strategies.

A complex matrix characterization approach, applied to cigarette smoke, that integrates multiple analytical methods and compound identification strategies for non-targeted liquid chromatography with high-resolution mass spectrometry.

RationaleFor the characterization of the chemical composition of complex matrices such as tobacco smoke, containing more than 6000 constituents, several analytical approaches have to be combined to increase compound coverage across the chemical space. Furthermore, the identification of unknown molecules requiring the implementation of additional confirmatory tools in the absence of reference standards, such as tandem mass spectrometry spectra comparisons and in silico prediction of mass spectra, is a major bottleneck.MethodsWe applied a combination of four chromatographic/ionization techniques (reversed‐phase (RP) – heated electrospray ionization (HESI) in both positive (+) and negative (−) modes, RP – atmospheric pressure chemical ionization (APCI) in positive mode, and hydrophilic interaction liquid chromatography (HILIC) – HESI positive) using a Thermo Q Exactive™ liquid chromatography/high‐resolution accurate mass spectrometry (LC/HRAM‐MS) platform for the analysis of 3R4F‐derived smoke. Compound identification was performed by using mass spectral libraries and in silico predicted fragments from multiple integrated databases.ResultsA total of 331 compounds with semi‐quantitative estimates ≥100 ng per cigarette were identified, which were distributed within the known chemical space of tobacco smoke. The integration of multiple LC/HRAM‐MS‐based chromatographic/ionization approaches combined with complementary compound identification strategies was key for maximizing the number of amenable compounds and for strengthening the level of identification confidence. A total of 50 novel compounds were identified as being present in tobacco smoke. In the absence of reference MS2 spectra, in silico MS2 spectra prediction gave a good indication for compound class and was used as an additional confirmatory tool for our integrated non‐targeted screening (NTS) approach.ConclusionsThis study presents a powerful chemical characterization approach that has been successfully applied for the identification of novel compounds in cigarette smoke. We believe that this innovative approach has general applicability and a huge potential benefit for the analysis of any complex matrices.

Screening of Polysorbate-80 Composition by High Resolution Mass Spectrometry with Rapid H/D Exchange.

Polysorbate (PS) is a widely used polymeric excipient in biotherapeutic formulations to stabilize and protect protein drugs. Commercial PS is a highly heterogeneous mixture of structurally related components. PS composition can impact the stabilizer performance of PS in formulated protein drugs. Characterization of PS heterogeneity is, however, analytically challenging. In this work, a high-throughput screening protocol is presented for the profiling of the PS-80 polysorbate form using high resolution mass spectrometry (HRMS) coupled with a rapid hydrogen/deuterium (H/D) exchange in deuterated methanol. The protocol takes advantage of accurate mass measurements from HRMS analysis and utilizes H/D exchange-induced mass shifts that are characteristic to structures (particularly the number of terminal hydroxyl groups) of PS molecules to definitively identify species. In particular, mass shifts caused by deuterium uptake were used (1) to confirm molecular identities assigned by accurate mass measurements (which adds an extra level of identification confidence) and (2) to differentiate isomers that have an identical mass (thus, undistinguishable by high mass accuracy), but differ in the number of terminal hydroxyls. These data were input to an automated searching algorithm against a molecular mass database covering over 17000 potential PS-80 molecular species. The identified species were then visualized with Kendrick Mass Defect plots. The analysis protocol identified and profiled over 180 species from PS-80 samples in a high-throughput fashion without requiring chromatographic separation to reduce complexity of mixtures or tandem mass spectrometric analysis to conduct structural elucidation.

Multi-modal Iris Recognition System based on Convolution Neural Network

Iris is most promising bio-metric trait for identification or authentication. Iris consists of patterns that are unique and highly random in nature .The discriminative property of iris pattern has attracted many researchers attention. The unimodal system, which uses only one bio-metric trait, suffers from limitation such as inter-class variation, intra-class variation and non-universality. The multi-modal bio-metric system has ability to overcome these drawbacks by fusing multiple biometric traits. In this paper, a multi-modal iris recognition system is proposed. The features are extracted using convolutional neural network and softmax classifier is used for multi-class classification. Finally, rank level fusion method is used to fuse right and left iris in order to improve the confidence level of identification. This method is tested on two data sets namely IITD and CASIA-Iris-V3.

Non-target screening of (semi-)volatiles in food-grade polymers by comparison of atmospheric pressure gas chromatography quadrupole time-of-flight and electron ionization mass spectrometry

Atmospheric pressure gas chromatography (APGC) coupled to quadrupole time-of-flight (QTOF) and electron ionization mass spectrometry together with commercial library search are two complementary techniques for non-target screening of volatile and semi-volatile compounds. Optimization was first conducted to achieve easier search of correspondent peaks between the two systems. Analytical strategy for the determination of volatile and semi-volatile compound with different identification confidence levels was then proposed and applied to food contact grade polypropylene (PP) samples. Identification was found to be much easier and less time-consuming especially when correspondent peak was found in the two systems with the help of library search, exact mass of precursor and fragment ions as well as Kovats Index (KI). The behavior of APGC-QTOF-MS was also further investigated. Apart from the M+. ion and the well-known adduct [M+H]+ others such as [M-3H + O]+, [M-3H+2O]+ and [M-H+3O]+ were also observed for n-alkanes. Besides, new reaction products were found, formed by diol compounds (1-Monostearoylglycerol, 2-Monostearoylglycerol and NX 8000K) and silanediol dimethyl, which would be a transformation product of the silicone base septum or the methyl 5% phenyl polysiloxane based column. These new compounds were only detected in APGC-MS-QTOF as EI-GC-MS was not enough sensitive for this purpose.

From untargeted LC-QTOF analysis to characterisation of opines in abalone adductor muscle: Theory meets practice.

Abalone have a unique ability to use pyruvate, various amino acids and dehydrogenases, to produce opines as means to prevent the accumulation of NADH during anaerobic conditions. In this study, the theoretical masses, formulae and fragment patterns of butylated opines were used to predict which of these compounds could be found in the abalone adductor muscle using untargeted liquid chromatography quadrupole time-of flight-mass spectrometry. These findings were validated using synthesised opine standards. In essence alanopine, lysopine, strombine and tauropine produced in abalone adductor muscle could be characterised using the highest identification confidence levels.

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An Interative Grahical User Interface for Maritime Security Services

Abstract. In order to analyse optical satellite images for maritime security issues in Near-Real-Time (NRT) an interactive graphical user interface (GUI) based on NASA World Wind was developed and is presented in this article. Targets or activities can be detected, measured and classified with this tool simply and quickly. The service uses optical satellite images, currently taken from 6 sensors: Worldview-1 and Worldview-2, Ikonos, Quickbird, GeoEye-1 and EROS-B. The GUI can also handle SAR-images, air-borne images or UAV images. Software configurations are provided in a job-order file and thus all preparation tasks, such as image installation are performed fully automatically. The imagery can be overlaid with vessels derived by an automatic detection processor. These potential vessel layers can be zoomed in by a single click and sorted with an adapted method. Further object properties, such as vessel type or confidence level of identification, can be added by the operator manually. The heading angle can be refined by dragging the vessel's head or switching it to 180° with a single click. Further vessels or other relevant objects can be added. The objects length, width, heading and position are calculated automatically from three clicks on top, bottom and an arbitrary point at one of the object’s longer side. In case of an Activity Detection, the detected objects can be grouped in area of interests (AOI) and classified, according to the ordered activities. All relevant information is finally written to an exchange file, after quality control and necessary correction procedures are performed. If required, image thumbnails can be cut around objects or around whole areas of interest and saved as separated, geo-referenced images.

Comparison of phenotypic methods and matrix-assisted laser desorption ionisation time-of-flight mass spectrometry for the identification of aero-tolerant Actinomyces spp. isolated from soft-tissue infections

Aero-tolerant Actinomyces spp. are an under-recognised cause of cutaneous infections, in part because identification using conventional phenotypic methods is difficult and may be inaccurate. Matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) is a promising new technique for bacterial identification, but with limited data on the identification of aero-tolerant Actinomyces spp. This study evaluated the accuracy of a phenotypic biochemical kit, MALDI-TOF MS and genotypic identification methods for the identification of this problematic group of organisms. Thirty aero-tolerant Actinomyces spp. were isolated from soft-tissue infections over a 2-year period. Species identification was performed by 16 s rRNA sequencing and genotypic results were compared with results obtained by API Coryne and MALDI-TOF MS. There was poor agreement between API Coryne and genotypic identification, with only 33% of isolates correctly identified to the species level. MALDI-TOF MS correctly identified 97% of isolates to the species level, with 33% of identifications achieved with high confidence scores. MALDI-TOF MS is a promising new tool for the identification of aero-tolerant Actinomyces spp., but improvement of the database is required in order to increase the confidence level of identification.

High Range Resolution Profiles as Motion-Invariant Features for Moving Ground Targets Identification in SAR-Based Automatic Target Recognition

A quantitative model analysis is presented to justify the extraction of high range resolution (HRR) profiles from synthetic aperture radar (SAR) images as motion-invariant features for identifying moving ground targets. A comparative study is conducted to assess the effectiveness in the identification process between using HRR profiles and SAR images as target signatures. The results indicate that HRR profiles are just as viable as SAR image for identification. Furthermore, a score-level multi-look fusion identification method has been investigated. It is found that a correct accurate identification rate of greater than 99 percent, a low false alarm rate, and a high level of identification confidence can be achieved, providing very robust performance.

Parallel post‐source decay for increasing protein identification confidence levels from 2‐D gels

Peptide mass fingerprinting (PMF) has over the years become one of the most commonly used tools for high-throughput analysis and identification of proteins. This method is applicable when relatively simple samples have to be analysed and it is commonly used for analysing proteins previously separated by 2-DE. The most common type of instrument used for this approach is the MALDI-TOF that has proved to be particularly suitable for the PMF analysis because of its characteristics of speed, robustness, sensitivity and automation. We have used a MALDI-TOF equipped with a novel parallel PSD capability (MALDI micro MX), to perform the analysis of two sets of different biological samples isolated by 2-DE. By using a method that integrates the data obtained by PMF analysis with the PSD data obtained in the same experiment, we show that the new multiplexed PSD solution increases the protein identification rate compared to the normal PMF approach. We also investigated the use of a charge-directed fragmentation modification reagent to improve the identification rate and confidence levels.

Comparison between traditional strategies and classification technique (SIMCA) in the identification of old proteinaceous binders

In this paper, we performed a comparison between commonly used strategies amino acid ratios (Aa ratios), two-dimensional ratio plots (2D-Plot) and statistical correlation factor (SCF) and a classification technique, soft independent modelling of class analogy (SIMCA), to identify protein binders present in old artwork samples. To do this, we used a natural standard collection of proteinaceous binders prepared in our laboratory using old recipes and eleven samples coming from Cultural Heritage, such as mural and easel paintings, manuscripts and polychrome sculptures from the 15–18th centuries. Protein binder samples were hydrolyzed and their constitutive amino acids were determined as PITC-derivatives using HPLC-DAD. Amino acid profile data were used to perform the comparison between the four different strategies mentioned above. Traditional strategies can lead to ambiguous or non-conclusive results. With SIMCA, it is possible to provide a more robust and less subjective identification knowing the confidence level of identification. As a standard, we used proteinaceous albumin (whole egg, yolk and glair); casein (goat, cow and sheep) and collagen (mammalian and fish). The process results in a more robust understanding of proteinaceous binding media in old artworks that makes it possible to distinguish them according to their origin.